using Gadfly, RDatasets
set_default_plot_size(21cm, 8cm)
p1 = plot(dataset("datasets", "iris"), x="SepalLength", y="SepalWidth",
Geom.point)
p2 = plot(dataset("datasets", "iris"), x="SepalLength", y="SepalWidth",
Stat.binmean, Geom.point)
hstack(p1,p2)
SepalLength
4
5
6
7
8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
3.98
4.00
4.02
4.04
4.06
4.08
4.10
4.12
4.14
4.16
4.18
4.20
4.22
4.24
4.26
4.28
4.30
4.32
4.34
4.36
4.38
4.40
4.42
4.44
4.46
4.48
4.50
4.52
4.54
4.56
4.58
4.60
4.62
4.64
4.66
4.68
4.70
4.72
4.74
4.76
4.78
4.80
4.82
4.84
4.86
4.88
4.90
4.92
4.94
4.96
4.98
5.00
5.02
5.04
5.06
5.08
5.10
5.12
5.14
5.16
5.18
5.20
5.22
5.24
5.26
5.28
5.30
5.32
5.34
5.36
5.38
5.40
5.42
5.44
5.46
5.48
5.50
5.52
5.54
5.56
5.58
5.60
5.62
5.64
5.66
5.68
5.70
5.72
5.74
5.76
5.78
5.80
5.82
5.84
5.86
5.88
5.90
5.92
5.94
5.96
5.98
6.00
6.02
6.04
6.06
6.08
6.10
6.12
6.14
6.16
6.18
6.20
6.22
6.24
6.26
6.28
6.30
6.32
6.34
6.36
6.38
6.40
6.42
6.44
6.46
6.48
6.50
6.52
6.54
6.56
6.58
6.60
6.62
6.64
6.66
6.68
6.70
6.72
6.74
6.76
6.78
6.80
6.82
6.84
6.86
6.88
6.90
6.92
6.94
6.96
6.98
7.00
7.02
7.04
7.06
7.08
7.10
7.12
7.14
7.16
7.18
7.20
7.22
7.24
7.26
7.28
7.30
7.32
7.34
7.36
7.38
7.40
7.42
7.44
7.46
7.48
7.50
7.52
7.54
7.56
7.58
7.60
7.62
7.64
7.66
7.68
7.70
7.72
7.74
7.76
7.78
7.80
7.82
7.84
7.86
7.88
7.90
7.92
7.94
7.96
7.98
8.00
4
6
8
7.6253.0875
7.1400000000000013.2
6.8571428571428563.0714285714285716
6.68000000000000153.0300000000000002
6.5,3.0
6.39999999999999952.9571428571428577
6.2999999999999992.8555555555555556
6.22.8249999999999997
6.052.7916666666666665
5.9000000000000013.0666666666666664
5.7999999999999992.8857142857142857
5.7000000000000013.1
5.60000000000000052.816666666666667
5.4428571428571433.207142857142857
5.2,3.425
5.10000000000000053.477777777777778
4.96253.05625
4.7714285714285713.185714285714286
4.4888888888888893.0777777777777775
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
2.75
3.00
3.25
3.50
2.70
2.75
2.80
2.85
2.90
2.95
3.00
3.05
3.10
3.15
3.20
3.25
3.30
3.35
3.40
3.45
3.50
2.750
2.755
2.760
2.765
2.770
2.775
2.780
2.785
2.790
2.795
2.800
2.805
2.810
2.815
2.820
2.825
2.830
2.835
2.840
2.845
2.850
2.855
2.860
2.865
2.870
2.875
2.880
2.885
2.890
2.895
2.900
2.905
2.910
2.915
2.920
2.925
2.930
2.935
2.940
2.945
2.950
2.955
2.960
2.965
2.970
2.975
2.980
2.985
2.990
2.995
3.000
3.005
3.010
3.015
3.020
3.025
3.030
3.035
3.040
3.045
3.050
3.055
3.060
3.065
3.070
3.075
3.080
3.085
3.090
3.095
3.100
3.105
3.110
3.115
3.120
3.125
3.130
3.135
3.140
3.145
3.150
3.155
3.160
3.165
3.170
3.175
3.180
3.185
3.190
3.195
3.200
3.205
3.210
3.215
3.220
3.225
3.230
3.235
3.240
3.245
3.250
3.255
3.260
3.265
3.270
3.275
3.280
3.285
3.290
3.295
3.300
3.305
3.310
3.315
3.320
3.325
3.330
3.335
3.340
3.345
3.350
3.355
3.360
3.365
3.370
3.375
3.380
3.385
3.390
3.395
3.400
3.405
3.410
3.415
3.420
3.425
3.430
3.435
3.440
3.445
3.450
3.455
3.460
3.465
3.470
3.475
3.480
3.485
3.490
3.495
3.500
2.5
3.0
3.5
SepalWidth
SepalLength
4
5
6
7
8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
3.98
4.00
4.02
4.04
4.06
4.08
4.10
4.12
4.14
4.16
4.18
4.20
4.22
4.24
4.26
4.28
4.30
4.32
4.34
4.36
4.38
4.40
4.42
4.44
4.46
4.48
4.50
4.52
4.54
4.56
4.58
4.60
4.62
4.64
4.66
4.68
4.70
4.72
4.74
4.76
4.78
4.80
4.82
4.84
4.86
4.88
4.90
4.92
4.94
4.96
4.98
5.00
5.02
5.04
5.06
5.08
5.10
5.12
5.14
5.16
5.18
5.20
5.22
5.24
5.26
5.28
5.30
5.32
5.34
5.36
5.38
5.40
5.42
5.44
5.46
5.48
5.50
5.52
5.54
5.56
5.58
5.60
5.62
5.64
5.66
5.68
5.70
5.72
5.74
5.76
5.78
5.80
5.82
5.84
5.86
5.88
5.90
5.92
5.94
5.96
5.98
6.00
6.02
6.04
6.06
6.08
6.10
6.12
6.14
6.16
6.18
6.20
6.22
6.24
6.26
6.28
6.30
6.32
6.34
6.36
6.38
6.40
6.42
6.44
6.46
6.48
6.50
6.52
6.54
6.56
6.58
6.60
6.62
6.64
6.66
6.68
6.70
6.72
6.74
6.76
6.78
6.80
6.82
6.84
6.86
6.88
6.90
6.92
6.94
6.96
6.98
7.00
7.02
7.04
7.06
7.08
7.10
7.12
7.14
7.16
7.18
7.20
7.22
7.24
7.26
7.28
7.30
7.32
7.34
7.36
7.38
7.40
7.42
7.44
7.46
7.48
7.50
7.52
7.54
7.56
7.58
7.60
7.62
7.64
7.66
7.68
7.70
7.72
7.74
7.76
7.78
7.80
7.82
7.84
7.86
7.88
7.90
7.92
7.94
7.96
7.98
8.00
4
6
8
5.9,3.0
6.2,3.4
6.5,3.0
6.3,2.5
6.7,3.0
6.7,3.3
6.8,3.2
5.8,2.7
6.9,3.1
6.7,3.1
6.9,3.1
6.0,3.0
6.4,3.1
6.3,3.4
7.7,3.0
6.1,2.6
6.3,2.8
6.4,2.8
7.9,3.8
7.4,2.8
7.2,3.0
6.4,2.8
6.1,3.0
6.2,2.8
7.2,3.2
6.7,3.3
6.3,2.7
7.7,2.8
5.6,2.8
6.9,3.2
6.0,2.2
7.7,2.6
7.7,3.8
6.5,3.0
6.4,3.2
5.8,2.8
5.7,2.5
6.8,3.0
6.4,2.7
6.5,3.2
7.2,3.6
6.7,2.5
7.3,2.9
4.9,2.5
7.6,3.0
6.5,3.0
6.3,2.9
7.1,3.0
5.8,2.7
6.3,3.3
5.7,2.8
5.1,2.5
6.2,2.9
5.7,2.9
5.7,3.0
5.6,2.7
5.0,2.3
5.8,2.6
6.1,3.0
5.5,2.6
5.5,2.5
5.6,3.0
6.3,2.3
6.7,3.1
6.0,3.4
5.4,3.0
6.0,2.7
5.8,2.7
5.5,2.4
5.5,2.4
5.7,2.6
6.0,2.9
6.7,3.0
6.8,2.8
6.6,3.0
6.4,2.9
6.1,2.8
6.3,2.5
6.1,2.8
5.9,3.2
5.6,2.5
6.2,2.2
5.8,2.7
5.6,3.0
6.7,3.1
5.6,2.9
6.1,2.9
6.0,2.2
5.9,3.0
5.0,2.0
5.2,2.7
6.6,2.9
4.9,2.4
6.3,3.3
5.7,2.8
6.5,2.8
5.5,2.3
6.9,3.1
6.4,3.2
7.0,3.2
5.0,3.3
5.3,3.7
4.6,3.2
5.1,3.8
4.8,3.0
5.1,3.8
5.0,3.5
4.4,3.2
4.5,2.3
5.0,3.5
5.1,3.4
4.4,3.0
4.9,3.6
5.5,3.5
5.0,3.2
4.9,3.1
5.5,4.2
5.2,4.1
5.4,3.4
4.8,3.1
4.7,3.2
5.2,3.4
5.2,3.5
5.0,3.4
5.0,3.0
4.8,3.4
5.1,3.3
4.6,3.6
5.1,3.7
5.4,3.4
5.1,3.8
5.7,3.8
5.1,3.5
5.4,3.9
5.7,4.4
5.8,4.0
4.3,3.0
4.8,3.0
4.8,3.4
5.4,3.7
4.9,3.1
4.4,2.9
5.0,3.4
4.6,3.4
5.4,3.9
5.0,3.6
4.6,3.1
4.7,3.2
4.9,3.0
5.1,3.5
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
2.0
2.5
3.0
3.5
4.0
4.5
2.0
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
3.0
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
4.0
4.1
4.2
4.3
4.4
4.5
1.99
2.00
2.01
2.02
2.03
2.04
2.05
2.06
2.07
2.08
2.09
2.10
2.11
2.12
2.13
2.14
2.15
2.16
2.17
2.18
2.19
2.20
2.21
2.22
2.23
2.24
2.25
2.26
2.27
2.28
2.29
2.30
2.31
2.32
2.33
2.34
2.35
2.36
2.37
2.38
2.39
2.40
2.41
2.42
2.43
2.44
2.45
2.46
2.47
2.48
2.49
2.50
2.51
2.52
2.53
2.54
2.55
2.56
2.57
2.58
2.59
2.60
2.61
2.62
2.63
2.64
2.65
2.66
2.67
2.68
2.69
2.70
2.71
2.72
2.73
2.74
2.75
2.76
2.77
2.78
2.79
2.80
2.81
2.82
2.83
2.84
2.85
2.86
2.87
2.88
2.89
2.90
2.91
2.92
2.93
2.94
2.95
2.96
2.97
2.98
2.99
3.00
3.01
3.02
3.03
3.04
3.05
3.06
3.07
3.08
3.09
3.10
3.11
3.12
3.13
3.14
3.15
3.16
3.17
3.18
3.19
3.20
3.21
3.22
3.23
3.24
3.25
3.26
3.27
3.28
3.29
3.30
3.31
3.32
3.33
3.34
3.35
3.36
3.37
3.38
3.39
3.40
3.41
3.42
3.43
3.44
3.45
3.46
3.47
3.48
3.49
3.50
3.51
3.52
3.53
3.54
3.55
3.56
3.57
3.58
3.59
3.60
3.61
3.62
3.63
3.64
3.65
3.66
3.67
3.68
3.69
3.70
3.71
3.72
3.73
3.74
3.75
3.76
3.77
3.78
3.79
3.80
3.81
3.82
3.83
3.84
3.85
3.86
3.87
3.88
3.89
3.90
3.91
3.92
3.93
3.94
3.95
3.96
3.97
3.98
3.99
4.00
4.01
4.02
4.03
4.04
4.05
4.06
4.07
4.08
4.09
4.10
4.11
4.12
4.13
4.14
4.15
4.16
4.17
4.18
4.19
4.20
4.21
4.22
4.23
4.24
4.25
4.26
4.27
4.28
4.29
4.30
4.31
4.32
4.33
4.34
4.35
4.36
4.37
4.38
4.39
4.40
4.41
4.42
4.43
4.44
4.45
4.46
4.47
4.48
4.49
4.50
2
3
4
5
SepalWidth
using DataFrames, Gadfly, Distributions
set_default_plot_size(21cm, 8cm)
x = -4:0.1:4
Da = [DataFrame(x=x, ymax=pdf.(Normal(μ),x), ymin=0.0, u="μ=$μ") for μ in [-1,1]]
Db = [DataFrame(x=randn(200).+μ, u="μ=$μ") for μ in [-1,1]]
p1 = plot(vcat(Da...), x=:x, y=:ymax, ymin=:ymin, ymax=:ymax, color=:u,
Geom.line, Geom.ribbon, Guide.ylabel("Density"), Theme(alphas=[0.6]),
Guide.colorkey(title="", pos=[2.5,0.6]), Guide.title("Parametric PDF")
)
p2 = plot(vcat(Db...), x=:x, color=:u, Theme(alphas=[0.6]),
Stat.density(bandwidth=0.5), Geom.polygon(fill=true, preserve_order=true),
Coord.cartesian(xmin=-4, xmax=4, ymin=0, ymax=0.4),
Guide.colorkey(title="", pos=[2.5,0.6]), Guide.title("Kernel PDF")
)
hstack(p1,p2)
x
-4
-2
0
2
4
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
-4.00
-3.95
-3.90
-3.85
-3.80
-3.75
-3.70
-3.65
-3.60
-3.55
-3.50
-3.45
-3.40
-3.35
-3.30
-3.25
-3.20
-3.15
-3.10
-3.05
-3.00
-2.95
-2.90
-2.85
-2.80
-2.75
-2.70
-2.65
-2.60
-2.55
-2.50
-2.45
-2.40
-2.35
-2.30
-2.25
-2.20
-2.15
-2.10
-2.05
-2.00
-1.95
-1.90
-1.85
-1.80
-1.75
-1.70
-1.65
-1.60
-1.55
-1.50
-1.45
-1.40
-1.35
-1.30
-1.25
-1.20
-1.15
-1.10
-1.05
-1.00
-0.95
-0.90
-0.85
-0.80
-0.75
-0.70
-0.65
-0.60
-0.55
-0.50
-0.45
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
1.45
1.50
1.55
1.60
1.65
1.70
1.75
1.80
1.85
1.90
1.95
2.00
2.05
2.10
2.15
2.20
2.25
2.30
2.35
2.40
2.45
2.50
2.55
2.60
2.65
2.70
2.75
2.80
2.85
2.90
2.95
3.00
3.05
3.10
3.15
3.20
3.25
3.30
3.35
3.40
3.45
3.50
3.55
3.60
3.65
3.70
3.75
3.80
3.85
3.90
3.95
4.00
-5
0
5
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
μ=-1
μ=1
0.0
0.1
0.2
0.3
0.4
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
0.40
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
0.018
0.020
0.022
0.024
0.026
0.028
0.030
0.032
0.034
0.036
0.038
0.040
0.042
0.044
0.046
0.048
0.050
0.052
0.054
0.056
0.058
0.060
0.062
0.064
0.066
0.068
0.070
0.072
0.074
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Kernel PDF
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h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
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Density
Parametric PDF
using CategoricalArrays
using Gadfly
set_default_plot_size(14cm, 8cm)
n = 400
group = repeat([-1, 1], inner=200)
x = randn(n) .+ group
plot(x=x, color=categorical(group), Guide.colorkey(title="", pos=[3.6,0.7]),
layer(Stat.density, Geom.line, Geom.polygon(fill=true, preserve_order=true), alpha=[0.4]),
layer(Stat.quantile_bars(quantiles=[0.05, 0.95]), Geom.segment),
Guide.title("Density with bars showing the central 90% CI"),
Guide.ylabel("Density"), Coord.cartesian(xmin=-4, xmax=4)
)
x
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h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
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Density
Density with bars showing the central 90% CI
using DataFrames, Gadfly, RDatasets, Statistics
set_default_plot_size(21cm, 8cm)
salaries = dataset("car","Salaries")
salaries.Salary /= 1000.0
salaries.Discipline = ["Discipline $(x)" for x in salaries.Discipline]
df = combine(groupby(salaries, [:Rank, :Discipline]), :Salary.=>mean)
df.label = string.(round.(Int, df.Salary_mean))
p1 = plot(df, x=:Discipline, y=:Salary_mean, color=:Rank,
Scale.x_discrete(levels=["Discipline A", "Discipline B"]),
label=:label, Geom.label(position=:centered), Stat.dodge(position=:stack),
Geom.bar(position=:stack)
)
p2 = plot(df, y=:Discipline, x=:Salary_mean, color=:Rank,
Coord.cartesian(yflip=true), Scale.y_discrete,
label=:label, Geom.label(position=:right), Stat.dodge(axis=:y),
Geom.bar(position=:dodge, orientation=:horizontal),
Scale.color_discrete(levels=["Prof", "AssocProf", "AsstProf"]),
Guide.yticks(orientation=:vertical), Guide.ylabel(nothing)
)
hstack(p1, p2)
Salary_mean
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Prof
AssocProf
AsstProf
Rank
133
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h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
Discipline B
Discipline A
Discipline B
Discipline A
Discipline
Discipline A
Discipline B
Prof
AsstProf
AssocProf
Rank
133
85
101
120
83
74
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
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Salary_mean
using DataFrames, Gadfly
set_default_plot_size(14cm, 8cm)
sigmoid(x) = 1 ./ (1 .+ exp.(-x))
npoints = 30
gshift, x = rand([0,2], npoints), range(-9, 9, length=npoints)
y, ye = sigmoid(x+gshift), 0.2*rand(npoints)
df = DataFrame(x=x, y=y, ymin=y-ye, ymax=y+ye, g=gshift)
plot(y=[sigmoid, x->sigmoid(x+2)], xmin=[-10], xmax=[10],
Geom.line, Stat.func(100), color=[0,2], Guide.xlabel("x"),
layer(df, x=:x, y=:y, ymin=:ymin, ymax=:ymax, color=:g,
Geom.point, Geom.yerrorbar, Stat.x_jitter(range=1)),
Scale.color_discrete_manual("deepskyblue","yellow3", levels=[0,2]),
Guide.colorkey(title="Function", labels=["Sigmoid(x)", "Sigmoid(x+2)"]),
Theme(errorbar_cap_length=0mm, key_position=:inside)
)
x
-10
-5
0
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-10
-9
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-1
0
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10.0
-10
0
10
9.0141046600311480.9998766054240137
8.6927540025953610.9999689322859989
8.1439459673205050.9995731370872474
7.5339262418893150.9992062365897446
6.6189131357520840.9998000496254946
5.547565393392760.9996281141773367
5.0757871084144220.999308435318234
4.3911606127179690.9987143089615129
4.1168057252619660.982612832728348
3.1354122445179220.9681328340340032
2.73212982377129960.9423020076914295
2.3910512889365950.8977447634316474
1.2028286926363470.9721241862548583
0.76812760129307690.9493594320914616
0.151905903768971230.5769694274020658
-0.249320961278248680.8441788063113398
-0.8281913982033450.7444001360376136
-1.58342552357296950.610229226597668
-2.10681175390071160.45700301154629586
-3.18894341318910430.05769799230857057
-3.2464350186055960.03186716596599677
-4.1491536301362420.017387167271651932
-4.6106589343894060.06567092533092088
-4.9267611518636330.036408609346018014
-5.8616504657392570.002741371708568133
-6.7756507565596650.010801164785603749
-7.01153531072300850.00583556787482027
-7.9328016384674130.00042686291275275794
-8.4381743082221340.00022951552431103904
-8.9822199994533580.0009110511944006454
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
Sigmoid(x)
Sigmoid(x+2)
Function
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1.49
1.50
-0.5
0.0
0.5
1.0
1.5
y
using Distributions, Gadfly, RDatasets
set_default_plot_size(21cm, 8cm)
iris, geyser = dataset.("datasets", ["iris", "faithful"])
df = combine(groupby(iris, :Species), :SepalLength=>(x->fit(Normal, x))=>:d)
ds2 = fit.([Normal, Uniform], [geyser.Eruptions])
yeqx(x=4:6) = layer(x=x, Geom.abline(color="gray80"))
xylabs = [Guide.xlabel("Theoretical q"), Guide.ylabel("Sample q")]
p1 = plot(df, x=:d, y=iris[:,1], color=:Species, Stat.qq, yeqx(4:8),
xylabs..., Guide.title("3 Samples, 1 Distribution"))
p2 = plot(geyser, x=ds2, y=:Eruptions, color=["Normal","Uniform"], Stat.qq,
yeqx(0:6), xylabs..., Guide.title("1 Sample, 2 Distributions"),
Theme(discrete_highlight_color=c->nothing, alphas=[0.5], point_size=2pt)
)
hstack(p1, p2)
Theoretical q
0
2
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6
8
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5.95
6.00
6.05
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6.35
6.40
6.45
6.50
6.55
6.60
6.65
6.70
6.75
6.80
6.85
6.90
6.95
7.00
7.05
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7.15
7.20
7.25
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7.35
7.40
7.45
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7.60
7.65
7.70
7.75
7.80
7.85
7.90
7.95
8.00
0
10
Normal
Uniform
Color
5.0870848708487085.067
5.0741697416974175.033
5.0612546125461265.0
5.0483394833948344.933
5.0354243542435424.933
5.022509225092254.933
5.0095940959409594.9
4.9966789667896684.8999999999999995
4.9837638376383764.883
4.9708487084870844.85
4.9579335793357924.833
4.9450184501845024.833
4.932103321033214.817
4.91918819188191854.817
4.9062730627306274.8
4.8933579335793364.8
4.8804428044280444.8
4.8675276752767534.8
4.8546125461254614.8
4.8416974169741694.8
4.8287822878228784.783
4.8158671586715874.767
4.8029520295202954.75
4.7900369003690034.732999999999999
4.7771217712177124.716000000000001
4.764206642066424.7
4.7512915129151294.7
4.7383763837638374.7
4.7254612546125454.7
4.71254612546125354.7
4.6996309963099634.7
4.68671586715867154.667
4.673800738007384.666999999999999
4.6608856088560894.650000000000001
4.6479704797047974.633
4.6350553505535064.633
4.6221402214022144.633
4.6092250922509224.617
4.596309963099634.6
4.5833948339483394.6
4.5704797047970484.6
4.5575645756457564.6
4.5446494464944644.583
4.5317343173431734.583
4.5188191881918824.583
4.505904059040594.583
4.4929889298892984.567
4.48007380073800654.567
4.46715867158671554.567
4.45424354243542454.549999999999999
4.4413284132841334.533
4.4284132841328414.533
4.4154981549815494.533
4.4025830258302594.533
4.3896678966789674.533
4.3767527675276754.517
4.3638376383763834.5
4.3509225092250924.5
4.33800738007384.5
4.3250922509225094.5
4.3121771217712174.5
4.2992619926199254.5
4.2863468634686344.5
4.2734317343173434.5
4.26051660516605154.4830000000000005
4.247601476014764.467
4.2346863468634694.467
4.2217712177121774.45
4.2088560885608864.45
4.1959409594095944.449999999999999
4.1830258302583024.433
4.1701107011070114.433
4.157195571955724.417
4.1442804428044284.417
4.1313653136531364.417
4.1184501845018454.417
4.1055350553505534.4
4.0926199261992624.383
4.079704797047974.367
4.0667896678966784.367
4.0538745387453874.367
4.04095940959409554.365999999999999
4.02804428044280454.3500000000000005
4.0151291512915134.35
4.0022140221402214.35
3.98929889298892974.35
3.97638376383763834.333
3.96346863468634644.333
3.95055350553505544.333
3.93763837638376354.333
3.9247232472324724.333
3.91180811808118064.317000000000001
3.8988929889298894.3
3.88597785977859774.3
3.87306273062730584.283
3.86014760147601484.283
3.8472324723247234.267
3.83431734317343144.267
3.821402214022144.25
3.80848708487084854.25
3.7955719557195574.25
3.78265682656826564.25
3.7697416974169744.2330000000000005
3.75682656826568234.233
3.74391143911439134.233
3.73099630996309944.2
3.7180811808118084.183
3.70516605166051654.167
3.6922509225092254.167
3.6793357933579334.167
3.66642066420664174.166999999999999
3.65350553505535074.150000000000001
3.6405904059040594.15
3.62767527675276744.15
3.6147601476014764.15
3.60184501845018454.133
3.58892988929889264.133
3.57601476014760164.117
3.56309963099630974.116999999999999
3.5501845018450184.1
3.53726937269372684.1
3.52435424354243534.083
3.5114391143911444.083
3.4985239852398524.083
3.4856088560885614.083
3.4726937269372694.083
3.4597785977859784.067
3.4468634686346864.066999999999999
3.43394833948339474.05
3.42103321033210334.033
3.40811808118081144.033
3.39520295202952044.000000000000001
3.38228782287822854.0
3.36937269372693754.0
3.35645756457564564.0
3.3435424354243544.0
3.33062730627306274.0
3.31771217712177123.967
3.304797047970483.966
3.2918819188191883.95
3.27896678966789653.95
3.2660516605166053.917
3.25313653136531363.917
3.2402214022140223.917
3.227306273062733.883
3.2143911439114393.85
3.20147601476014733.85
3.18856088560885633.8330000000000006
3.17564575645756443.833
3.1627306273062733.833
3.14981549815498153.833
3.13690036900368963.8329999999999997
3.12398523985239863.817
3.11107011070110673.767
3.09815498154981533.767
3.0852398523985243.75
3.07232472324723243.733
3.0594095940959413.7170000000000005
3.04649446494464953.683
3.0335793357933583.6
3.0206642066420663.6
3.00774907749077473.6
2.99483394833948323.6
2.9819188191881923.567
2.96900369003690033.5669999999999993
2.9560885608856093.5
2.94317343173431743.5
2.93025830258302563.450000000000001
2.91734317343173453.417
2.90442804428044273.367
2.8915129151291513.333
2.87859778597785983.333
2.8656826568265683.317
2.8527675276752773.067
2.8398523985239852.8999999999999995
2.8269372693726942.883
2.8140221402214022.800000000000001
2.80110701107011062.6330000000000022
2.7881918819188192.617
2.77527675276752772.483
2.76236162361623632.417
2.74944649446494442.417
2.7365313653136532.4
2.72361623616236152.4
2.710701107011072.3829999999999996
2.69778597785977862.367
2.6848708487084872.3500000000000005
2.67195571955719572.333
2.6590405904059042.317
2.6461254612546132.3
2.6332103321033212.2829999999999995
2.62029520295202942.267
2.6073800738007382.25
2.59446494464944652.25
2.5815498154981552.233
2.56863468634686362.233
2.5557195571955722.2170000000000005
2.54280442804428032.2
2.5298892988929892.2
2.51697416974169742.2
2.5040590405904062.183
2.49114391143911452.167
2.47822878228782262.167
2.46531365313653162.15
2.45239852398523972.133
2.43948339483394832.1
2.4265682656826572.1
2.41365313653136542.1
2.4007380073800742.083
2.3878228782287822.083
2.3749077490774912.067
2.3619926199261992.033
2.34907749077490772.033
2.33616236162361622.017
2.3232472324723252.017
2.31033210332103332.017
2.29741697416974142.0
2.284501845018452.0
2.27158671586715852.0
2.2586715867158672.0
2.24575645756457561.983
2.2328413284132841.983
2.21992619926199271.9829999999999999
2.20701107011070131.9670000000000003
2.194095940959411.967
2.1811808118081181.967
2.16826568265682651.95
2.1553505535055351.933
2.14243542435424361.933
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3.9364308175025474.3
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3.3025597575592733.817
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3.2381630839296273.683
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3.20567617618859923.6
3.19479734368662263.6
3.18389172969073.567
3.17295819765513533.5669999999999993
3.16199559198203333.5
3.15100273704495363.5
3.1399784361747013.450000000000001
3.1289214706048063.417
3.11783059837408953.367
3.10670455318354933.333
3.09554204320460973.333
3.08434174983560543.317
3.07310232640312763.067
3.0618223968046622.8999999999999995
3.05050055408867272.883
3.039135358968042.800000000000001
3.02772533826245072.6330000000000022
3.01626898326502162.617
3.00476474802811032.483
2.9932110475628622.417
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2.96994870433216862.4
2.95823667885122442.4
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2.04905119053336151.867
2.02534961276698631.85
2.00099759246400751.85
1.97594664282682091.833
1.9501426006893841.833
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1.89602437783565031.833
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1.80739559208819231.833
1.7754685517416031.817
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1.70724063348453941.817
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1.45094077165142331.783
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1.19622296029091221.75
1.11027824617048761.75
1.00819592762460971.75
0.88118338271587811.733
0.70999926526821171.7
0.43546026381077231.667
h,j,k,l,arrows,drag to pan
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?
0
1
2
3
4
5
6
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
2.25
2.50
2.75
3.00
3.25
3.50
3.75
4.00
4.25
4.50
4.75
5.00
5.25
5.50
5.75
6.00
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
0.40
0.42
0.44
0.46
0.48
0.50
0.52
0.54
0.56
0.58
0.60
0.62
0.64
0.66
0.68
0.70
0.72
0.74
0.76
0.78
0.80
0.82
0.84
0.86
0.88
0.90
0.92
0.94
0.96
0.98
1.00
1.02
1.04
1.06
1.08
1.10
1.12
1.14
1.16
1.18
1.20
1.22
1.24
1.26
1.28
1.30
1.32
1.34
1.36
1.38
1.40
1.42
1.44
1.46
1.48
1.50
1.52
1.54
1.56
1.58
1.60
1.62
1.64
1.66
1.68
1.70
1.72
1.74
1.76
1.78
1.80
1.82
1.84
1.86
1.88
1.90
1.92
1.94
1.96
1.98
2.00
2.02
2.04
2.06
2.08
2.10
2.12
2.14
2.16
2.18
2.20
2.22
2.24
2.26
2.28
2.30
2.32
2.34
2.36
2.38
2.40
2.42
2.44
2.46
2.48
2.50
2.52
2.54
2.56
2.58
2.60
2.62
2.64
2.66
2.68
2.70
2.72
2.74
2.76
2.78
2.80
2.82
2.84
2.86
2.88
2.90
2.92
2.94
2.96
2.98
3.00
3.02
3.04
3.06
3.08
3.10
3.12
3.14
3.16
3.18
3.20
3.22
3.24
3.26
3.28
3.30
3.32
3.34
3.36
3.38
3.40
3.42
3.44
3.46
3.48
3.50
3.52
3.54
3.56
3.58
3.60
3.62
3.64
3.66
3.68
3.70
3.72
3.74
3.76
3.78
3.80
3.82
3.84
3.86
3.88
3.90
3.92
3.94
3.96
3.98
4.00
4.02
4.04
4.06
4.08
4.10
4.12
4.14
4.16
4.18
4.20
4.22
4.24
4.26
4.28
4.30
4.32
4.34
4.36
4.38
4.40
4.42
4.44
4.46
4.48
4.50
4.52
4.54
4.56
4.58
4.60
4.62
4.64
4.66
4.68
4.70
4.72
4.74
4.76
4.78
4.80
4.82
4.84
4.86
4.88
4.90
4.92
4.94
4.96
4.98
5.00
5.02
5.04
5.06
5.08
5.10
5.12
5.14
5.16
5.18
5.20
5.22
5.24
5.26
5.28
5.30
5.32
5.34
5.36
5.38
5.40
5.42
5.44
5.46
5.48
5.50
5.52
5.54
5.56
5.58
5.60
5.62
5.64
5.66
5.68
5.70
5.72
5.74
5.76
5.78
5.80
5.82
5.84
5.86
5.88
5.90
5.92
5.94
5.96
5.98
6.00
0
10
Sample q
1 Sample, 2 Distributions
Theoretical q
4
5
6
7
8
9
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
3.98
4.00
4.02
4.04
4.06
4.08
4.10
4.12
4.14
4.16
4.18
4.20
4.22
4.24
4.26
4.28
4.30
4.32
4.34
4.36
4.38
4.40
4.42
4.44
4.46
4.48
4.50
4.52
4.54
4.56
4.58
4.60
4.62
4.64
4.66
4.68
4.70
4.72
4.74
4.76
4.78
4.80
4.82
4.84
4.86
4.88
4.90
4.92
4.94
4.96
4.98
5.00
5.02
5.04
5.06
5.08
5.10
5.12
5.14
5.16
5.18
5.20
5.22
5.24
5.26
5.28
5.30
5.32
5.34
5.36
5.38
5.40
5.42
5.44
5.46
5.48
5.50
5.52
5.54
5.56
5.58
5.60
5.62
5.64
5.66
5.68
5.70
5.72
5.74
5.76
5.78
5.80
5.82
5.84
5.86
5.88
5.90
5.92
5.94
5.96
5.98
6.00
6.02
6.04
6.06
6.08
6.10
6.12
6.14
6.16
6.18
6.20
6.22
6.24
6.26
6.28
6.30
6.32
6.34
6.36
6.38
6.40
6.42
6.44
6.46
6.48
6.50
6.52
6.54
6.56
6.58
6.60
6.62
6.64
6.66
6.68
6.70
6.72
6.74
6.76
6.78
6.80
6.82
6.84
6.86
6.88
6.90
6.92
6.94
6.96
6.98
7.00
7.02
7.04
7.06
7.08
7.10
7.12
7.14
7.16
7.18
7.20
7.22
7.24
7.26
7.28
7.30
7.32
7.34
7.36
7.38
7.40
7.42
7.44
7.46
7.48
7.50
7.52
7.54
7.56
7.58
7.60
7.62
7.64
7.66
7.68
7.70
7.72
7.74
7.76
7.78
7.80
7.82
7.84
7.86
7.88
7.90
7.92
7.94
7.96
7.98
8.00
8.02
8.04
8.06
8.08
8.10
8.12
8.14
8.16
8.18
8.20
8.22
8.24
8.26
8.28
8.30
8.32
8.34
8.36
8.38
8.40
8.42
8.44
8.46
8.48
8.50
8.52
8.54
8.56
8.58
8.60
8.62
8.64
8.66
8.68
8.70
8.72
8.74
8.76
8.78
8.80
8.82
8.84
8.86
8.88
8.90
8.92
8.94
8.96
8.98
9.00
3
6
9
setosa
versicolor
virginica
Species
8.1443160768333257.7
7.98153371220608057.7
7.8790715274783987.7
7.80248445937887.7
7.7405366305681417.6
7.6880813512981347.399999999999998
7.64231173771522257.3
7.6015209137885237.2
7.5645900179858117.2
7.53074408809550467.2
7.4994231881564137.1
7.4702089694365737.0
7.4427802468495466.9
7.4168848056209076.9
7.3923207876703326.9
7.368923989159476.9
7.3465589444852066.8
7.3251125144014366.8
7.3044891765631786.8
7.28460750169199766.7
7.2653974731711856.7
7.2467984180665566.7
7.2287573889257676.7
7.2112278829988166.7
7.19416881751862656.7
7.1775437017381256.7
7.1613199618866146.7
7.1454683862223276.600000000000001
7.1299626653134816.599999999999998
7.1147790085016516.5
7.0998958218129256.5
7.0852934358111536.5
7.0709538743309526.5
7.0568606568946766.5
7.04299862905643356.4
7.0293538160348116.4
7.0159132958721836.4
7.0026650890502496.4
6.9895980620410576.4
6.97670184271259956.4
6.9639667458620276.399999999999999
6.95138370743611046.3
6.9389442262319356.3
6.9266403120617826.3
6.9144644395231936.3
6.9024095066450236.3
6.8904687977880436.3
6.8786359502684386.3
6.8669049242477696.3
6.85526997549610156.3
6.8437256306883076.2
6.8322666649385976.2
6.8208880813166746.2
6.8095850921214296.199999999999998
6.7983531017160316.1
6.7871876907520536.1
6.7760846016308036.1
6.7650397250676346.1
6.7540490876402556.1
6.7431088402151546.1
6.73221524715768556.0
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h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
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Sample q
3 Samples, 1 Distribution
using Compose, Gadfly, RDatasets
set_default_plot_size(21cm,8cm)
salaries = dataset("car","Salaries")
salaries.Salary /= 1000.0
salaries.Discipline = ["Discipline $(x)" for x in salaries.Discipline]
p = plot(salaries[salaries.Rank.=="Prof",:], x=:YrsService, y=:Salary,
color=:Sex, xgroup = :Discipline,
Geom.subplot_grid(Geom.point,
layer(Stat.smooth(method=:lm, levels=[0.95, 0.99]), Geom.line, Geom.ribbon)),
Scale.xgroup(levels=["Discipline A", "Discipline B"]),
Guide.colorkey(title="", pos=[0.43w, -0.4h]),
Theme(point_size=2pt, alphas=[0.5])
)
YrsService by
Discipline B
Discipline A
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57.5
60.0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
10.2
10.4
10.6
10.8
11.0
11.2
11.4
11.6
11.8
12.0
12.2
12.4
12.6
12.8
13.0
13.2
13.4
13.6
13.8
14.0
14.2
14.4
14.6
14.8
15.0
15.2
15.4
15.6
15.8
16.0
16.2
16.4
16.6
16.8
17.0
17.2
17.4
17.6
17.8
18.0
18.2
18.4
18.6
18.8
19.0
19.2
19.4
19.6
19.8
20.0
20.2
20.4
20.6
20.8
21.0
21.2
21.4
21.6
21.8
22.0
22.2
22.4
22.6
22.8
23.0
23.2
23.4
23.6
23.8
24.0
24.2
24.4
24.6
24.8
25.0
25.2
25.4
25.6
25.8
26.0
26.2
26.4
26.6
26.8
27.0
27.2
27.4
27.6
27.8
28.0
28.2
28.4
28.6
28.8
29.0
29.2
29.4
29.6
29.8
30.0
30.2
30.4
30.6
30.8
31.0
31.2
31.4
31.6
31.8
32.0
32.2
32.4
32.6
32.8
33.0
33.2
33.4
33.6
33.8
34.0
34.2
34.4
34.6
34.8
35.0
35.2
35.4
35.6
35.8
36.0
36.2
36.4
36.6
36.8
37.0
37.2
37.4
37.6
37.8
38.0
38.2
38.4
38.6
38.8
39.0
39.2
39.4
39.6
39.8
40.0
40.2
40.4
40.6
40.8
41.0
41.2
41.4
41.6
41.8
42.0
42.2
42.4
42.6
42.8
43.0
43.2
43.4
43.6
43.8
44.0
44.2
44.4
44.6
44.8
45.0
45.2
45.4
45.6
45.8
46.0
46.2
46.4
46.6
46.8
47.0
47.2
47.4
47.6
47.8
48.0
48.2
48.4
48.6
48.8
49.0
49.2
49.4
49.6
49.8
50.0
50.2
50.4
50.6
50.8
51.0
51.2
51.4
51.6
51.8
52.0
52.2
52.4
52.6
52.8
53.0
53.2
53.4
53.6
53.8
54.0
54.2
54.4
54.6
54.8
55.0
55.2
55.4
55.6
55.8
56.0
56.2
56.4
56.6
56.8
57.0
57.2
57.4
57.6
57.8
58.0
58.2
58.4
58.6
58.8
59.0
59.2
59.4
59.6
59.8
60.0
0
100
21,145.028
20,138.0
27,142.5
38,93.519
49,186.96
28,144.309
33,128.25
27,142.023
10,107.986
35,150.376
31,162.15
38,114.596
17,124.312
11,106.231
15,137.317
25,128.464
12,145.0
23,98.053
38,151.445
19,145.098
10,105.45
9,116.518
60,192.253
23,134.778
37,151.65
15,124.714
31,162.221
15,161.101
23,104.428
15,135.027
16,134.55
45,67.559
20,129.6
10,145.2
21,170.0
11,119.5
11,146.0
11,145.35
19,126.2
7,128.4
39,111.35
20,163.2
18,120.0
33,162.2
2,96.545
5,165.0
17,152.5
17,160.4
40,119.7
22,114.5
33,189.409
18,122.1
22,133.7
9,180.0
19,153.75
10,107.5
30,134.0
23,101.0
22,150.0
5,141.136
11,142.467
14,147.349
25,111.751
20,134.185
24,93.164
19,151.575
18,181.257
19,130.664
16,167.284
8,105.89
19,176.5
16,137.167
21,118.971
9,111.168
12,128.148
26,144.651
27,156.938
28,119.015
27,112.696
14,127.512
5,153.303
23,126.933
25,133.217
26,106.689
14,102.235
7,129.676
20,123.683
38,166.024
25,172.272
37,152.708
14,132.825
18,122.96
20,144.64
16,135.585
28,150.743
19,193.0
3,150.48
23,113.398
34,92.391
19,100.131
45,146.856
2,126.32
36,91.412
17,111.512
31,99.418
12,101.0
31,109.785
21,117.704
9,106.639
11,108.875
28,126.621
25,140.096
19,151.768
28,98.193
15,114.778
19,94.384
38,231.545
27,101.299
2,146.5
31,125.196
21,155.75
9,117.256
4,132.261
8,118.223
20,101.0
3,117.15
18,104.8
18,129.0
20,119.25
45,147.765
23,175.0
41,141.5
39,115.0
16,173.2
18,139.75
15,95.329
25,101.738
19,150.564
30,103.106
19,151.292
19,166.605
18,186.023
36,119.45
15,109.305
27,139.219
9,114.33
21,125.192
44,105.0
23,172.505
38,150.68
26,103.649
19,103.275
26,136.66
7,109.707
20,110.515
31,134.69
30,131.95
10,115.435
40,101.036
43,205.5
30,138.771
15,109.646
11,121.946
14,109.954
35,107.309
40,88.709
6,146.8
35,100.351
19,94.35
9,108.1
7,92.05
15,166.8
28,122.5
31,111.35
44,144.05
4,105.26
13,170.5
16,127.1
36,88.6
43,72.3
11,148.8
18,126.3
36,97.15
7,107.3
9,183.8
28,168.5
7,174.5
27,150.5
27,115.8
43,155.865
51,57.8
27,103.6
38,136.5
46,100.6
18,107.1
27,163.2
48,107.2
6,93.0
18,194.8
40,143.25
7,103.7
44,89.65
10,104.35
43,143.94
30,134.8
35,99.0
31,126.0
26,121.2
45,107.55
30,92.55
22,140.3
7,116.45
12,132.0
8,102.0
39,109.0
7,204.0
23,128.8
18,101.1
23,91.1
11,90.45
23,84.273
23,108.2
37,102.6
30,122.875
6,96.2
40,77.202
25,114.0
17,81.7
19,117.555
19,148.75
27,91.0
38,133.9
11,88.175
20,122.4
35,87.8
11,106.608
18,152.664
14,105.668
14,108.262
18,136.0
25,168.635
57,76.84
30,113.278
26,155.5
49,78.162
22,96.614
22,97.262
32,124.309
14,115.313
36,117.515
29,148.5
9,120.806
0,105.0
37,104.279
16,112.429
31,131.205
28,113.543
23,134.885
8,106.294
19,113.068
30,93.904
31,102.58
26,89.565
36,137.0
23,124.75
34,103.45
0
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Male
Female
Salary
using DataFrames, Gadfly
set_default_plot_size(14cm, 8cm)
x = range(0.1, stop=4.9, length=30)
D = DataFrame(x=x, y=x.+randn(length(x)))
p = plot(D, x=:x, y=:y, Geom.point,
layer(Stat.smooth(method=:lm, levels=[0.90,0.99]), Geom.line, Geom.ribbon(fill=false)),
Theme(lowlight_color=c->"gray", line_style=[:solid, :dot])
)
x
0
1
2
3
4
5
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
0.40
0.42
0.44
0.46
0.48
0.50
0.52
0.54
0.56
0.58
0.60
0.62
0.64
0.66
0.68
0.70
0.72
0.74
0.76
0.78
0.80
0.82
0.84
0.86
0.88
0.90
0.92
0.94
0.96
0.98
1.00
1.02
1.04
1.06
1.08
1.10
1.12
1.14
1.16
1.18
1.20
1.22
1.24
1.26
1.28
1.30
1.32
1.34
1.36
1.38
1.40
1.42
1.44
1.46
1.48
1.50
1.52
1.54
1.56
1.58
1.60
1.62
1.64
1.66
1.68
1.70
1.72
1.74
1.76
1.78
1.80
1.82
1.84
1.86
1.88
1.90
1.92
1.94
1.96
1.98
2.00
2.02
2.04
2.06
2.08
2.10
2.12
2.14
2.16
2.18
2.20
2.22
2.24
2.26
2.28
2.30
2.32
2.34
2.36
2.38
2.40
2.42
2.44
2.46
2.48
2.50
2.52
2.54
2.56
2.58
2.60
2.62
2.64
2.66
2.68
2.70
2.72
2.74
2.76
2.78
2.80
2.82
2.84
2.86
2.88
2.90
2.92
2.94
2.96
2.98
3.00
3.02
3.04
3.06
3.08
3.10
3.12
3.14
3.16
3.18
3.20
3.22
3.24
3.26
3.28
3.30
3.32
3.34
3.36
3.38
3.40
3.42
3.44
3.46
3.48
3.50
3.52
3.54
3.56
3.58
3.60
3.62
3.64
3.66
3.68
3.70
3.72
3.74
3.76
3.78
3.80
3.82
3.84
3.86
3.88
3.90
3.92
3.94
3.96
3.98
4.00
4.02
4.04
4.06
4.08
4.10
4.12
4.14
4.16
4.18
4.20
4.22
4.24
4.26
4.28
4.30
4.32
4.34
4.36
4.38
4.40
4.42
4.44
4.46
4.48
4.50
4.52
4.54
4.56
4.58
4.60
4.62
4.64
4.66
4.68
4.70
4.72
4.74
4.76
4.78
4.80
4.82
4.84
4.86
4.88
4.90
4.92
4.94
4.96
4.98
5.00
0
5
4.95.1236639415945415
4.734482758620694.0857910741253916
4.5689655172413794.842611204779619
4.4034482758620695.195002504701055
4.23793103448275853.4375754021151135
4.0724137931034484.107922059526255
3.9068965517241383.992767976624603
3.74137931034482743.1102867960007323
3.5758620689655171.8598553056163665
3.4103448275862073.506423176253704
3.24482758620689673.888036205840213
3.0793103448275863.7097949046331724
2.9137931034482764.751394901784543
2.74827586206896561.8294619728000718
2.58275862068965531.9857062888307766
2.41724137931034472.489270577398729
2.25172413793103441.8653621575453705
2.0862068965517241.8320121533789155
1.92068965517241372.882931073697085
1.75517241379310350.3927434572513049
1.5896551724137931.1669398573016456
1.42413793103448282.1121293554397504
1.25862068965517240.8253280905880598
1.09310344827586210.1632698913543228
0.9275862068965517-0.7137276968158517
0.76206896551724131.7868092494125287
0.5965517241379310.6064197782701288
0.43103448275862066-1.0777001238387638
0.2655172413793103-0.3182382032287451
0.1-0.2225696498949116
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
-2
0
2
4
6
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
-2.00
-1.95
-1.90
-1.85
-1.80
-1.75
-1.70
-1.65
-1.60
-1.55
-1.50
-1.45
-1.40
-1.35
-1.30
-1.25
-1.20
-1.15
-1.10
-1.05
-1.00
-0.95
-0.90
-0.85
-0.80
-0.75
-0.70
-0.65
-0.60
-0.55
-0.50
-0.45
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
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0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
1.45
1.50
1.55
1.60
1.65
1.70
1.75
1.80
1.85
1.90
1.95
2.00
2.05
2.10
2.15
2.20
2.25
2.30
2.35
2.40
2.45
2.50
2.55
2.60
2.65
2.70
2.75
2.80
2.85
2.90
2.95
3.00
3.05
3.10
3.15
3.20
3.25
3.30
3.35
3.40
3.45
3.50
3.55
3.60
3.65
3.70
3.75
3.80
3.85
3.90
3.95
4.00
4.05
4.10
4.15
4.20
4.25
4.30
4.35
4.40
4.45
4.50
4.55
4.60
4.65
4.70
4.75
4.80
4.85
4.90
4.95
5.00
5.05
5.10
5.15
5.20
5.25
5.30
5.35
5.40
5.45
5.50
5.55
5.60
5.65
5.70
5.75
5.80
5.85
5.90
5.95
6.00
-2
0
2
4
6
y
using Gadfly, Random
set_default_plot_size(14cm, 8cm)
Random.seed!(1234)
plot(x=rand(25), y=rand(25), Stat.step, Geom.line)
x
0.0
0.5
1.0
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0.040
0.045
0.050
0.055
0.060
0.065
0.070
0.075
0.080
0.085
0.090
0.095
0.100
0.105
0.110
0.115
0.120
0.125
0.130
0.135
0.140
0.145
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0.155
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0.165
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0.185
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0.285
0.290
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0.300
0.305
0.310
0.315
0.320
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0.335
0.340
0.345
0.350
0.355
0.360
0.365
0.370
0.375
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0.385
0.390
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0.400
0.405
0.410
0.415
0.420
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0.430
0.435
0.440
0.445
0.450
0.455
0.460
0.465
0.470
0.475
0.480
0.485
0.490
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0.515
0.520
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0.530
0.535
0.540
0.545
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0.555
0.560
0.565
0.570
0.575
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1
h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
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1
y
using Gadfly, Distributions, Random
set_default_plot_size(14cm, 8cm)
Random.seed!(1234)
plot(x=rand(1:4, 500), y=rand(500), Stat.x_jitter(range=0.5), Geom.point)
x
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h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
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y
using Gadfly, Random
set_default_plot_size(14cm, 8cm)
Random.seed!(1234)
plot(x=rand(10), y=rand(10), Stat.xticks(ticks=[0.0, 0.1, 0.9, 1.0]), Geom.point)
x
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0.1
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1.0
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h,j,k,l,arrows,drag to pan
i,o,+,-,scroll,shift-drag to zoom
r,dbl-click to reset
c for coordinates
? for help
?
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1
y